Abstract
Data mining is widely used in today’s dynamic business environment as a manager’s decision making tool, however, not many applications have been used in accounting areas where accountants deal with large amounts of operational as well as financial data. The purpose of this research is to propose a multiple criteria linear programming (MCLP) approach to data mining for bankruptcy prediction. A multiple criteria linear programming data mining approach has recently been applied to credit card portfolio management. This approach has proven to be robust and powerful even for a large sample size using a huge financial database. The results of the MCLP approach in a bankruptcy prediction study are promising as this approach performs better than traditional multiple discriminant analysis or logit analysis using financial data. Similar approaches can be applied to other accounting areas such as fraud detection, detection of tax evasion, and an audit-planning tool for financially distressed firms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Altman, E.: Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance 23(3), 589–609 (1968)
Altman, E., Haldeman, R.G., Narayanan, P.: ZETA Analysis: A New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking and Finance 1(1), 29–54 (1977)
Barniv, R., Agarwal, A., Leach, R.: Predicting Bankruptcy Resolution. Journal of Business Finance & Accounting 29(3/4), 497–520 (2002)
Beaver, W.: Financial Rations as Predictors of Failure. Empirical Research in Accounting, Selected Studies. Supplement to the Journal of Accounting Research, 71–111 (1966)
Begley, J., Ming, J., Watts, S.: Bankruptcy Classification Errors in the 1980s: An Empirical Analysis of Altman’s and Ohlson’s Models. Review of Accounting Studies 1, 267–284 (1996)
Bose, I., Mahapatra, R.K.: Business Data Mining—A Machine Learning Perspective. Information and Management 39, 211–225 (2001)
Chan, C., Lewis, B.: A Basic Primer on Data Mining. Information Systems Management, 56–60 (Fall 2002)
Freed, N., Glover, F.: Evaluating Alternative Linear Programming Models to Solve the Two-Group Discriminant Problem. Decision Sciences 17, 151–162 (1986)
Grice, J.S., Dugan, M.T.: The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher. Review of Quantitative Finance and Accounting 17, 151–166 (2001)
Grice, J.S., Ingram, R.W.: Tests of Generalizability of Altman’s Bankruptcy Prediction Model. Journal of Business Research 54, 53–61 (2001)
Gupta, Y.P., Rao, R.P., Baggi, P.K.: Linear Goal Programming as an Alternative to Multivariate Discriminant Analysis: A Not. Journal of Business, Finance, and Accounting 17(4), 593–598 (1990)
Hotchkiss, E.S.: Post-Bankruptcy Performance and Management Turnover. Journal of Finance 50(1), 67–84 (1995)
Jones, F.L.: Current Techniques in Bankruptcy Prediction. Journal of Accounting Literature 6, 131–164 (1987)
Koehler, G.J., Erenguc, S.S.: Minimizing Misclassifications in Linear Discriminant Analysis. Decision Sciences 21, 63–85 (1990)
Kou, G., Liu, X., Peng, Y., Shi, Y., Wise, M., Xu, W.: Multiple Criteria Linear Programming Approach to Data Mining: Models, Algorithm Designs and Software Development. Optimization Methods and Software 18, 453–473 (2003)
Kou, G., Shi, Y.: Linux based Multiple Linear Programming Classification Program: version 1.0. College of Information Science and Technology, University of Nebraska-Omaha, Omaha, NE 68182, USA (2002)
McKee, T.E.: Developing a Bankruptcy Prediction Model via Rough Sets Theory. International Journal of Intelligent Systems in Accounting, Finance & Management 9, 159–173 (2000)
Mossman, C., Bell, G., Swartz, L.M., Turtle, H.: An Empirical Comparison of Bankruptcy Models. Financial Review 33, 35–53 (1998)
Nanda, S., Pendharkar, P.: Linear Models for Minimizing Misclassification Costs in Bankruptcy Prediction. International Journal of Intelligent Systems in Accounting, Finance & Management 10, 155–168 (2001)
Ohlson, J.: Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18(1), 109–131 (1980)
Park, C.S., Han, I.: A Case-Based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction. Expert Systems with Applications 23(3), 255–264 (2002)
Platt, D.H., Platt, M.B.: Development of a Class of Stable Predictive Variables: The Case of Bankruptcy Prediction. Journal of Business Finance & Accounting, 31–51 (Spring 1990)
Platt, D.H., Platt, M.B.: A Re-Examination of the Effectiveness of the Bankruptcy Process. Journal of Business Finance & Accounting 29(9/10), 1209–1237 (Spring 2002)
Pompe, P.P.M., Feelders, J.: Using Machine Learning, Neural Networks, and Statistics to Predict Corporate Bankruptcy. Microcomputers in Civil Engineering 12, 267–276 (1997)
SAS/OR User’s Guide, SAS Institute Inc., Cary, NC (1990)
Shi, Y.: Multiple Criteria multiple Constraint-Levels Linear Programming: Concepts, Techniques and Applications. World Scientific Publishing, River Edge (2001)
Shi, Y., Peng, Y., Xu, W., Tang, X.: Data Mining via Multiple Criteria Linear Programming: Applications in Credit Card Portfolio Management. International Journal of Information Technology & Decision Making 1(1), 131–151 (2002)
Shi, Y., Wise, M., Luo, M., Lin, Y.: Data Mining in Credit Card Portfolio Management: A Multiple Criteria Decision Making Approach. In: Koksakan, M., Zionts, S. (eds.) Multiple Criteria Decision Making in the New Millennium, pp. 427–436 (2001)
Shi, Y., Yu, P.L.: Goal Setting and Compromise Solutions. In: Karpak, B., Zionts, S. (eds.) Multiple Criteria Decision Making and Risk Analysis Using Microcomputers, pp. 165–204. Springer, Berlin (1989)
Shin, K.S., Lee, Y.J.: A Genetic Algorithm Application in Bankruptcy Prediction Modeling. Expert Systems with Applications 23(3), 321–328 (2002)
Sung, T.K., Chang, N., Lee, G.: Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction. Journal of Management Information Systems 16(1), 63–85 (1999)
Wilson, R.L., Sharda, R.: Bankruptcy Prediction using Neural Networks. Decision Support Systems 11, 545–557 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kwak, W., Shi, Y., Cheh, J.J., Lee, H. (2004). Multiple Criteria Linear Programming Data Mining Approach: An Application for Bankruptcy Prediction. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-30537-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23987-1
Online ISBN: 978-3-540-30537-8
eBook Packages: Computer ScienceComputer Science (R0)